Abstract:
The presentation will discuss recent developments in land data assimilation concerning the assimilation of terrestrial water storage (TWS) and soil moisture observations from satellite sensors into land surface models. Data from the Gravity Recovery and Climate Experiment (GRACE) system of satellites provides large-scale, monthly estimates of TWS. The paper will describe the assimilation of GRACE-derived monthly TWS anomalies for each of the four major sub-basins of the Mississippi into the Catchment Land Surface Model (CLSM) using an ensemble Kalman smoother, providing improved simulation of water storage and fluxes in the Mississippi River basin, as evaluated against independent measurements. Compared with the model-only CLSM simulation, assimilation estimates of groundwater variability exhibited enhanced skill with respect to measured groundwater in all four sub-basins. Assimilation also significantly increased the correlation between simulated TWS and gauged river flow for all four sub-basins and for the Mississippi River itself. In addition, model performance was evaluated for eight smaller watersheds within the Mississippi basin, all of which are smaller than the scale of GRACE observations. In seven of eight cases, GRACE assimilation led to increased correlation between TWS estimates and gauged river flow, indicating that data assimilation has considerable potential to downscale GRACE data for hydrological applications.

The Soil Moisture Active and Passive (SMAP) mission (planned launch in 2014) will make simultaneous active (radar) and passive (radiometer) measurements in the 1.26-1.43 GHz range (L-band). These measurements are directly related to surface soil moisture (in the top 5 cm of the soil column). Several of the key applications targeted by SMAP, however, require knowledge of root zone soil moisture (~top 1 m of the soil column). The foremost objective of the SMAP Level 4 Surface and Root-Zone Soil Moisture (L4_SM) product is to fill this gap and provide estimates of root zone soil moisture that are informed by and consistent with SMAP observations. Such estimates are obtained by merging SMAP observations with estimates from a land surface model in a soil moisture data assimilation system. The land surface model component of the assimilation system is driven with observations-based surface meteorological forcing data, including precipitation, which is the most important driver for soil moisture. The model also encapsulates knowledge of key land surface processes, including the vertical transfer of soil moisture between the surface and root zone reservoirs. Finally, the model interpolates and extrapolates SMAP observations in time and in space. The L4_SM product thus provides a comprehensive and consistent picture of land surface hydrological conditions based on SMAP observations and complementary information from a variety of sources. The assimilation algorithm considers the respective uncertainties of each component and yields a product that is superior to satellite or model data alone. Error estimates for the L4_SM product are generated as a by-product of the data assimilation system.